Data Scientist - Remote Sensing

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors. Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 700 experts from 35+ countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success. The Data Scientist – Remote Sensing role focuses on building and operationalising robust, production-grade data products derived from satellite and drone imagery, sitting at the intersection of applied machine learning, computer vision, and geospatial image processing. The ideal candidate is a strong generalist data scientist who is equally comfortable wrangling real-world data of different nature, training and evaluating machine learning and image analysis models, and shipping maintainable production pipelines. Domain expertise in remote sensing or satellite image processing is a significant asset but not a prerequisite: we will onboard an intellectually curious and motivated candidate on the specifics of our data. Working closely with data engineers and software engineers, the role spans the full lifecycle from exploration and prototyping to production deployment and monitoring, with a strong emphasis on robustness, scalability, and real-world applicability.   Responsibilities

Develop and maintain production-grade machine learning and image analysis pipelines.

Apply statistical, machine learning, computer vision, or signal-processing techniques to satellite and drone imagery datasets.

Analyse and process satellite imagery (SAR, optical, and others) or geospatial time series to detect, classify, and monitor physical activity and assets over time.

Contribute to the integration of multiple satellite or drone data providers and heterogeneous image sources.

Collaborate with data engineers and software engineers to integrate model outputs into APIs and customer-facing products.

Improve model robustness, performance, and interpretability over time.

Translate research or experimental code into reliable, production-ready workflows.

Document methodologies and share knowledge to support team-wide best practices.

Skills and Experience

3–6 years of experience in data science, applied machine learning, computer vision, or remote sensing / geospatial engineering.

Strong proficiency in Python for data processing, modelling, and pipeline development (numpy, pandas, scikit-learn, etc.).

Solid experience with data manipulation, cleaning, and feature engineering on real-world, noisy datasets.

Experience working with satellite imagery, geospatial data, and time-series datasets.

Proven track record of turning experimental or research code into production-ready workflows.

Solid understanding of software engineering best practices (version control, testing, code review).

Nice to have

Significant experience in general Computer Vision, including object detection, semantic segmentation, change detection, or anomaly detection.

Proficiency with CV/ML frameworks such as PyTorch, TensorFlow, or OpenCV.

Experience working with multiple commercial or open-source satellite data providers.

Experience with drone (UAV) imagery processing and advanced geometry computation (e.g., photogrammetry, coordinate transformations, or 3D reconstruction).

Knowledge of Radiometry and Radiometric Calibration.

Familiarity with data orchestration tools like Airflow.

Cloud infrastructure experience (AWS/GCP) and database knowledge (SQL/Postgres/PostGIS).